Emerging Job Roles in Analytics

Traditionally, business intelligence has been helping firms analyze their historical data. However, tables turned when Data Analytics gave the power to predict events and suggest actions.

Due to its potential, the past few years have witnessed a phenomenal growth in the reach of data analytics. Although some domains, like marketing, sales, and customer relationship management, started using data analytics much before any other field, but today almost all sectors identified – be it research, government organizations, healthcare, services, manufacturing, airlines or even sports – are realizing the value data analytics has and are continuously roping in competent professionals to help them milk the several opportunities that they might have missed otherwise.

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With increasing adoption of Analytics, rising alongside the relatively new big data platforms, are some new job roles, like Data Scientist, Data Engineers, Big Data analysts and Data Visualization analysts. Let’s take a look at these emerging roles:

Big Data Analyst: requires strong data mining skills, including ETL (data Extraction-Transformation-Loading), data validation, data exploration and aggregation. Here atleast a basic working knowledge of Big Data platforms, like Hadoop and MapReduce, Pig and Hive is mandatory. Using scripting languages like R and Python, a Big Data analyst should be able to generate business insights after processing the raw data.The role also involves working with IT teams and some job roles may even require statistical analysis capabilities.

Data Scientist: A data scientist primarily deconstructs structured and unstructured data, explores it through the use of “Predictive and Prescriptive Analytics”, relates the insights obtained to the objectives of the firm and communicates their value to different business functions (like IT, Marketing, Management, Operations, etc). This role requires inter-disciplinary skills and high degree of proficiency with analytics languages, like SAS/ R/ Python as well as a good working knowledge of platforms like Hadoop. A data scientist is expected to combine the skills of analyzing big data, using advanced statistical and machine learning algorithms, harnessing the power of social media and wielding investigation tools to tell a compelling story that a layman can understand.

Data Engineer: A data engineer designs, builds and manages the information or ‘big data’ infrastructure. Essentially, they develop the architecture and systems which drive analysis and processing of data as well as ensure efficient functioning of these systems. They also gather and process raw data, integrate innovative solutions and algorithms into production systems and support business decisions with ad hoc analysis. Data engineers are required to work closely with IT teams and Data Scientist. Their role is quite instrumental for successful implementation of enterprise-wide analytics.

Data Visualization Analyst: They utilize BI and visual analytics tools like, Tableau, QlikView etc. to analyze a large amount of data and present a compelling story in visual formats – such as infographics, maps and other multidimensional charts and dashboard. Data Visualization has got to do with “Descriptive” analytics where explanation and exploration are the two main goals. As competition is increasing and company performance is becoming necessary to track, the demand for visual analysts is increasing.

Despite the demarcations between job titles, skills and roles often overlap or are interchangeably used and the organization offers a position where the expertise required is a combination of two or more roles.

At end of the day, big data analytics behemoth is basically looking for two types of people – those who can channelize a large amount of information and those who can translate business problems to analytical problems, while the ability to communicate remains intrinsic to both roles.

As more and more businesses and government organizations across the world are going to put their faith in data-driven decisions, a plethora of roles previously unheard of – such as Internet of Things (IoT) architect, marketing technologist, technology broker and chief data officer – will be introduced into the fold of data analytics. The skills required for such roles will be in tandem with the development of existing technologies and, undoubtedly, with new technologies unfurling, exciting opportunities for professional will come up to learn and grow.

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Ankita Gupta is the Principal Consultant and Co-founder of AnalytixLabs.
Starting her career with McKinsey where she helped set-up the Analytics
team in 2004, she has had a core focus in the Marketing Analytics area. Post
almost 8 years with McKinsey, she then moved to Fidelity Investments to
own the analytics for their UK client-side businesses. As a part of her work,
she has worked across various industries like Healthcare, Telecom, Banking,
Hi- Tech across various countries like Japan, Russia, UK, US concentrating in
domains like Choice Based Modelling, Customer Life cycle Management and
Pricing.
By education, Ankita holds a Maths Hons degree from St Stephen’s College,
Delhi University and has also completed her MBA from ISB, Hyderabad.